This paper is in the following e-collection/theme issue:

Connected Health Conference 2017 (1) Use and User Demographics of mHealth (344)

Published on in Vol 2, No 2 (2018): Jul-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11977, first published .
Acceptance of Mobile Health Apps for Disease Management Among People With Multiple Sclerosis: Web-Based Survey Study

Acceptance of Mobile Health Apps for Disease Management Among People With Multiple Sclerosis: Web-Based Survey Study

Acceptance of Mobile Health Apps for Disease Management Among People With Multiple Sclerosis: Web-Based Survey Study

Authors of this article:

Jennifer Apolinário-Hagen1 Author Orcid Image ;   Mireille Menzel1 Author Orcid Image ;   Severin Hennemann2 Author Orcid Image ;   Christel Salewski1 Author Orcid Image
Article Authors Cited by (43) Tweetations (32) Metrics

Journals

  1. Zhang Y, Liu C, Luo S, Xie Y, Liu F, Li X, Zhou Z. Factors Influencing Patients’ Intentions to Use Diabetes Management Apps Based on an Extended Unified Theory of Acceptance and Use of Technology Model: Web-Based Survey. Journal of Medical Internet Research 2019;21(8):e15023 View
  2. Jongen P, ter Veen G, Lemmens W, Donders R, van Noort E, Zeinstra E. The Interactive Web-Based Program MSmonitor for Self-Management and Multidisciplinary Care in Persons With Multiple Sclerosis: Quasi-Experimental Study of Short-Term Effects on Patient Empowerment. Journal of Medical Internet Research 2020;22(3):e14297 View
  3. Salgado T, Tavares J, Oliveira T. Drivers of Mobile Health Acceptance and Use From the Patient Perspective: Survey Study and Quantitative Model Development. JMIR mHealth and uHealth 2020;8(7):e17588 View
  4. Alexander S, Peryer G, Gray E, Barkhof F, Chataway J. Wearable technologies to measure clinical outcomes in multiple sclerosis: A scoping review. Multiple Sclerosis Journal 2021;27(11):1643 View
  5. Thomas S, Pulman A, Thomas P, Collard S, Jiang N, Dogan H, Davies Smith A, Hourihan S, Roberts F, Kersten P, Pretty K, Miller J, Stanley K, Gay M. Digitizing a Face-to-Face Group Fatigue Management Program: Exploring the Views of People With Multiple Sclerosis and Health Care Professionals Via Consultation Groups and Interviews. JMIR Formative Research 2019;3(2):e10951 View
  6. Nasseri N, Ghezelbash E, Zhai Y, Patra S, Riemann-Lorenz K, Heesen C, Rahn A, Stellmann J. Feasibility of a smartphone app to enhance physical activity in progressive MS: a pilot randomized controlled pilot trial over three months. PeerJ 2020;8:e9303 View
  7. Apolinário-Hagen J, Hennemann S, Fritsche L, Drüge M, Breil B. Determinant Factors of Public Acceptance of Stress Management Apps: Survey Study. JMIR Mental Health 2019;6(11):e15373 View
  8. Brew-Sam N, Chib A, Rossmann C. Differential influences of social support on app use for diabetes self-management – a mixed methods approach. BMC Medical Informatics and Decision Making 2020;20(1) View
  9. Robinson A, Slight R, Husband A, Slight S. Designing the Optimal Digital Health Intervention for Patients’ Use Before and After Elective Orthopedic Surgery: Qualitative Study. Journal of Medical Internet Research 2021;23(3):e25885 View
  10. Xie Z, Kalun Or C. Acceptance of mHealth by Elderly Adults: A Path Analysis. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2020;64(1):755 View
  11. Gromisch E, Turner A, Haselkorn J, Lo A, Agresta T. Mobile health (mHealth) usage, barriers, and technological considerations in persons with multiple sclerosis: a literature review. JAMIA Open 2021;4(3) View
  12. De Angelis M, Lavorgna L, Carotenuto A, Petruzzo M, Lanzillo R, Brescia Morra V, Moccia M. Digital Technology in Clinical Trials for Multiple Sclerosis: Systematic Review. Journal of Clinical Medicine 2021;10(11):2328 View
  13. Damerau M, Teufel M, Musche V, Dinse H, Schweda A, Beckord J, Steinbach J, Schmidt K, Skoda E, Bäuerle A. Determining Acceptance of e-Mental Health Interventions in Digital Psychodiabetology Using a Quantitative Web-Based Survey: Cross-sectional Study. JMIR Formative Research 2021;5(7):e27436 View
  14. Mokhberdezfuli M, Ayatollahi H, Naser Moghadasi A, Taiar R. A Smartphone-based Application for Self-Management in Multiple Sclerosis. Journal of Healthcare Engineering 2021;2021:1 View
  15. REMY C, VALET M, STOQUART G, EL SANKARI S, VAN PESCH V, DE HAAN A, LEJEUNE T. Telecommunication and rehabilitation for patients with multiple sclerosis: access and willingness to use. A cross-sectional study. European Journal of Physical and Rehabilitation Medicine 2020;56(4) View
  16. Wattanapisit A, Amaek W, Wattanapisit S, Tuangratananon T, Wongsiri S, Pengkaew P. Challenges of Implementing an mHealth Application for Personalized Physical Activity Counselling in Primary Health Care: A Qualitative Study. International Journal of General Medicine 2021;Volume 14:3821 View
  17. Sora B, Nieto R, Montesano del Campo A, Armayones M. Acceptance and use of telepsychology from clients’ perspective: perceived advantages and barriers (Preprint). JMIR Mental Health 2020 View
  18. KC B, Alrasheedy A, Goh B, Blebil A, Bangash N, Mohamed Ibrahim M, Rehman I. The Types and Pattern of Use of Mobile Health Applications Among the General Population: A Cross-Sectional Study from Selangor, Malaysia. Patient Preference and Adherence 2021;Volume 15:1755 View
  19. Thomas S, Pulman A, Dogan H, Jiang N, Passmore D, Pretty K, Fairbanks B, Davies Smith A, Thomas P. Creating a Digital Toolkit to Reduce Fatigue and Promote Quality of Life in Multiple Sclerosis: Participatory Design and Usability Study. JMIR Formative Research 2021;5(12):e19230 View
  20. Van Baelen F, De Regge M, Larivière B, Verleye K, Schelfout S, Eeckloo K. Role of Social and App-Related Factors in Behavioral Engagement With mHealth for Improved Well-being Among Chronically Ill Patients: Scenario-Based Survey Study. JMIR mHealth and uHealth 2022;10(8):e33772 View
  21. Morgan K, Wong A, Walker K, Desai R, Knepper T, Newland P. A Mobile Phone Text Messaging Intervention to Manage Fatigue for People With Multiple Sclerosis, Spinal Cord Injury, and Stroke: Development and Usability Testing. JMIR Formative Research 2022;6(12):e40166 View
  22. Hong G, Smith M, Lin S. The AI Will See You Now: Feasibility and Acceptability of a Conversational AI Medical Interviewing System. JMIR Formative Research 2022;6(6):e37028 View
  23. Cheung Y, Lam P, Lam T, Lam H, Li C. Technology Acceptance Among Patients With Hemophilia in Hong Kong and Their Expectations of a Mobile Health App to Promote Self-management: Survey Study. JMIR Formative Research 2021;5(9):e27985 View
  24. Choi W, Chang S, Yang Y, Jung S, Lee S, Chun J, Kim D, Lee W, Choi I. Study of the factors influencing the use of MyData platform based on personal health record data sharing system. BMC Medical Informatics and Decision Making 2022;22(1) View
  25. De Regge M, Van Caelenberg E, Van Belle N, Eeckloo K, Coppens M. Encouraging Digital Patient Portal Use in Ambulatory Surgery: A Mixed Method Research of Patients and Health Care Professionals Experiences and Perceptions. Journal of PeriAnesthesia Nursing 2022;37(5):691 View
  26. Dionisi S, Giannetta N, Di Simone E, Ricciardi F, Liquori G, De Leo A, Moretti L, Napoli C, Di Muzio M, Orsi G. The Use of mHealth in Orthopedic Surgery: A Scoping Review. International Journal of Environmental Research and Public Health 2021;18(23):12549 View
  27. Barrios L, Amon R, Oldrati P, Hilty M, Holz C, Lutterotti A. Cognitive fatigability assessment test (cFAST): Development of a new instrument to assess cognitive fatigability and pilot study on its association to perceived fatigue in multiple sclerosis. DIGITAL HEALTH 2022;8:205520762211177 View
  28. Schretzlmaier P, Hecker A, Ammenwerth E. Suitability of the Unified Theory of Acceptance and Use of Technology 2 Model for Predicting mHealth Acceptance Using Diabetes as an Example: Qualitative Methods Triangulation Study. JMIR Human Factors 2022;9(1):e34918 View
  29. Philippi P, Baumeister H, Apolinário-Hagen J, Ebert D, Hennemann S, Kott L, Lin J, Messner E, Terhorst Y. Acceptance towards digital health interventions – Model validation and further development of the Unified Theory of Acceptance and Use of Technology. Internet Interventions 2021;26:100459 View
  30. Ma Y, Zhong X, Lin B, He W. Factors Influencing the Intention of MSM to Use the PrEP Intelligent Reminder System. Risk Management and Healthcare Policy 2021;Volume 14:4739 View
  31. Schretzlmaier P, Hecker A, Ammenwerth E. Extension of the Unified Theory of Acceptance and Use of Technology 2 model for predicting mHealth acceptance using diabetes as an example: a cross-sectional validation study. BMJ Health & Care Informatics 2022;29(1):e100640 View
  32. Kerwagen F, Fuchs K, Ullrich M, Schulze A, Straka S, Krop P, Latoschik M, Gilbert F, Kunz A, Fette G, Störk S, Ertl M. Usability of a mHealth Solution using Speech Recognition for Point-of-care Diagnostic Management. Journal of Medical Systems 2023;47(1) View
  33. Nittas V, Zecca C, Kamm C, Kuhle J, Chan A, von Wyl V, Mbada C. Digital health for chronic disease management: An exploratory method to investigating technology adoption potential. PLOS ONE 2023;18(4):e0284477 View
  34. Yao Y, Li Z, He Y, Zhang Y, Guo Z, Lei Y, Zhao Q, Li D, Zhang Z, Zhang Y, Liao X. Factors affecting wearable ECG device adoption by general practitioners for atrial fibrillation screening: cross-sectional study. Frontiers in Public Health 2023;11 View
  35. Wendrich K, Krabbenborg L. Negotiating with digital self-monitoring: A qualitative study on how patients with multiple sclerosis use and experience digital self-monitoring within a scientific study. Health: An Interdisciplinary Journal for the Social Study of Health, Illness and Medicine 2024;28(3):333 View
  36. Gibson B, Rosser B, Schneider J, Forshaw M, Manzoni G. The role of uncertainty intolerance in adjusting to long-term physical health conditions: A systematic review. PLOS ONE 2023;18(6):e0286198 View
  37. Heesen C, Berger T, Riemann-Lorenz K, Krause N, Friede T, Pöttgen J, Meyer B, Lühmann D. Mobile health interventions in multiple sclerosis: A systematic review. Multiple Sclerosis Journal 2023;29(14):1709 View
  38. Araújo I, Grilo A, Silva C. Portuguese Validation of the Unified Theory of Acceptance and Use of Technology Scale (UTAUT) to a COVID-19 Mobile Application: A Pilot Study. Healthcare 2023;11(13):1916 View
  39. Picco E, Miglioretti M, Le Blanc P. Sustainable employability, technology acceptance and task performance in workers collaborating with cobots: a pilot study. Cognition, Technology & Work 2024;26(1):139 View
  40. Schröder S, Buntrock C, Neumann L, Müller J, Fromberger P. Acceptance of a Web-Based Intervention in Individuals Who Committed Sexual Offenses Against Children: Cross-Sectional Study. JMIR Formative Research 2024;8:e48880 View
  41. Park Y, Tak Y, Kim I, Lee H, Lee J, Lee J, Lee Y. User Experience and Extended Technology Acceptance Model in Commercial Health Care App Usage Among Patients With Cancer: Mixed Methods Study. Journal of Medical Internet Research 2024;26:e55176 View

Books/Policy Documents

  1. Ndlovu B, Chipangura B, Singh S. Proceedings of Ninth International Congress on Information and Communication Technology. View
  2. Kokila ­, Jain R, Jeswal R, Ansari Z. AI Healthcare Applications and Security, Ethical, and Legal Considerations. View